Inference Optimization

Deploying LLMs is challenging due to their significant computational and memory requirements. Efficiently running these models necessitates the use of specialized accelerators, such as GPUs or TPUs, which can parallelize operations and achieve higher throughput. While some tasks, like document generation, can be processed in batches overnight, others require low latency and fast generation, such as code completion. As a result, optimizing the inference process – how these models make predictions based on input data – is critical for many practical applications. This includes reducing the time it takes to generate the first token (latency), increasing the number of tokens generated per second (throughput), and minimizing the memory footprint of LLMs.

Indeed, naive deployment approaches lead to poor hardware utilization and underwhelming throughput and latency. Fortunately, a variety of optimization techniques have emerged to dramatically speed...